| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| task_categories: |
| - text-classification |
| size_categories: |
| - 10K<n<100K |
| pretty_name: Distributed Ledger Technology (DLT) / Blockchain Sentiment News |
| dataset_info: |
| features: |
| - name: timestamp |
| dtype: string |
| - name: title |
| dtype: string |
| - name: description |
| dtype: string |
| - name: text |
| dtype: string |
| - name: market_direction |
| dtype: |
| class_label: |
| names: |
| '0': neutral |
| '1': bearish |
| '2': bullish |
| - name: engagement_quality |
| dtype: |
| class_label: |
| names: |
| '0': neutral |
| '1': liked |
| '2': disliked |
| - name: content_characteristics |
| dtype: |
| class_label: |
| names: |
| '0': neutral |
| '1': important |
| '2': lol |
| - name: vote_counts |
| struct: |
| - name: bearish |
| dtype: int32 |
| - name: bullish |
| dtype: int32 |
| - name: liked |
| dtype: int32 |
| - name: disliked |
| dtype: int32 |
| - name: important |
| dtype: int32 |
| - name: lol |
| dtype: int32 |
| - name: total_votes |
| dtype: int32 |
| - name: source_url |
| dtype: string |
| - name: url |
| dtype: string |
| - name: total_tokens |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 22639057 |
| num_examples: 23301 |
| download_size: 12118601 |
| dataset_size: 22639057 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| tags: |
| - DLT |
| - Blockchain |
| - Cryptocurrencies |
| - Cryptocurrency |
| - Bitcoin |
| - Ethereum |
| - XRP |
| - Hedera |
| --- |
| |
| # DLT-Sentiment-News |
|
|
| [Paper](https://huggingface.co/papers/2602.22045) | [Code](https://github.com/dlt-science/DLT-Corpus) |
|
|
| ## Dataset Description |
|
|
| ### Dataset Summary |
|
|
| DLT-Sentiment-News is a specialized sentiment analysis dataset for the Distributed Ledger Technology (DLT) domain. It addresses the lack of high-quality labeled data that captures domain-specific sentiment expressed by cryptocurrency community members. |
|
|
| The dataset contains **23,301 examples** with **1.85 million tokens** (average 79.51 tokens per example), spanning from **January 2021 to May 2025**. Each example includes cryptocurrency news headlines and descriptions with multi-dimensional sentiment labels crowdsourced from active community members on the CryptoPanic platform. |
|
|
| This dataset is part of the DLT-Corpus collection. For related datasets, see: https://huggingface.co/collections/ExponentialScience/dlt-corpus-68e44e40d4e7a3bd7a224402 |
|
|
| ### Supported Tasks |
|
|
| - **Sentiment Analysis**: Multi-dimensional sentiment classification for DLT and cryptocurrency content |
| - **Market Sentiment Studies**: Analyzing how cryptocurrency communities perceive market-related news |
| - **Content Quality Assessment**: Evaluating which content cryptocurrency users find valuable |
| - **Engagement Prediction**: Understanding what drives positive or negative community engagement |
| - **Model Evaluation**: Benchmarking domain-specific sentiment models |
|
|
| ### Languages |
|
|
| English (en) |
|
|
| ## Dataset Structure |
|
|
| ### Data Fields |
|
|
| Each example in the dataset contains the following fields: |
|
|
| - **title**: Headline of the cryptocurrency news article |
| - **description**: Brief description or summary of the article |
| - **text**: Combined title and description text |
| - **timestamp**: Date and time when the article was posted |
| - **market_direction**: Sentiment about market direction (bullish, bearish, neutral) |
| - **engagement_quality**: Community assessment of content importance (important, lol, neutral) |
| - **content_characteristics**: User engagement type (liked, disliked, neutral) |
| - **vote_counts**: Detailed breakdown of votes for each sentiment category |
| - **total_votes**: Total number of community votes received |
| - **source_url**: URL of the original news source |
| - **url**: CryptoPanic URL for the article |
| - **total_tokens**: Total number of tokens in the text |
| |
| ### Label Distribution |
| |
| The dataset includes three independent sentiment dimensions: |
| |
| **Market Direction:** |
| - `bullish`: Positive outlook on market/price movement |
| - `bearish`: Negative outlook on market/price movement |
| - `neutral`: Balanced or unclear market direction |
| |
| **Engagement Quality:** |
| - `important`: Content deemed significant by the community |
| - `lol`: Content considered humorous or not serious |
| - `neutral`: Standard content without strong quality signal |
| |
| **Content Characteristics:** |
| - `liked`: Positively received by the community |
| - `disliked`: Negatively received by the community |
| - `neutral`: Mixed or neutral community reception |
| |
| ### Data Splits |
| |
| This is a single corpus without predefined splits. Users should create their own train/validation/test splits based on their specific research needs. Consider temporal splits to avoid data leakage when studying market trends. |
| |
| ## Dataset Creation |
| |
| ### Curation Rationale |
| |
| DLT-Sentiment-News was created to support sentiment analysis research in the DLT domain with data that reflects authentic community perspectives. Unlike general sentiment datasets, this captures: |
| |
| - **Domain expertise**: Labels from active cryptocurrency users with market knowledge |
| - **Multi-dimensional sentiment**: Separate dimensions for market outlook, content quality, and engagement |
| - **Community consensus**: Aggregated opinions from multiple users rather than single annotators |
| - **Market context**: Sentiment tied to real cryptocurrency news and events |
| |
| ### Source Data |
| |
| #### Data Collection |
| |
| The dataset was collected from **CryptoPanic**, a cryptocurrency news aggregation platform where community members vote on news articles across multiple sentiment categories. |
| |
| **Collection Details:** |
| - Data collected via CryptoPanic's free API between March and May 2025 |
| - Coverage period: January 2021 to May 2025 |
| - Only articles meeting minimum vote thresholds included (median minimum votes) |
| - All content is publicly available news headlines and descriptions |
| |
| #### Data Processing |
| |
| The collection and labeling process involved: |
| |
| 1. **Article retrieval**: Collecting news articles with community votes from CryptoPanic |
| 2. **Vote normalization**: Calculating vote percentages by total engagement for each article |
| 3. **Minimum threshold filtering**: Excluding articles with insufficient community engagement (below median votes) |
| 4. **Percentile-based classification**: Using 25th and 75th percentiles as boundaries to assign labels |
| 5. **Quality control**: Ensuring balanced representation across sentiment categories |
| |
| ### Annotations |
| |
| #### Annotation Process |
| |
| **Crowdsourced Community Voting:** |
| - Active cryptocurrency community members on CryptoPanic vote on news articles |
| - Users select from predefined sentiment categories for each dimension |
| - Votes reflect genuine community sentiment and domain expertise |
| |
| **Label Assignment:** |
| - Percentile-based classification mitigates popularity bias |
| - Articles below 25th percentile labeled as negative category |
| - Articles above 75th percentile labeled as positive category |
| - Articles between percentiles labeled as neutral category |
| - Applied independently for each sentiment dimension |
| |
| #### Who are the annotators? |
| |
| Active cryptocurrency community members on the CryptoPanic platform. These annotators possess domain expertise and genuine interest in DLT/cryptocurrency news, providing more relevant sentiment labels than general crowdworkers. |
| |
| ### Personal and Sensitive Information |
| |
| This dataset contains only publicly available cryptocurrency news headlines and descriptions. No personal or confidential data is included. Individual voter information is not included - only aggregated vote counts and percentages are retained. |
| |
| ## Considerations for Using the Data |
| |
| ### Social Impact of Dataset |
| |
| This dataset can enable: |
| |
| - **Positive impacts**: Better understanding of cryptocurrency community sentiment, improved market analysis tools, advancement of domain-specific NLP research, more accurate sentiment detection |
| - **Potential negative impacts**: Could be misused for market manipulation, creating misleading investment systems, or amplifying market volatility through automated trading |
| |
| **Researchers should implement appropriate safeguards and ethical guidelines when working with this data.** |
| |
| ### Discussion of Biases |
| |
| Potential biases include: |
| |
| - **Platform bias**: Only reflects CryptoPanic users, not the entire cryptocurrency community |
| - **Language bias**: Only English-language news articles are included |
| - **Temporal bias**: More recent years may have different sentiment patterns than earlier periods |
| - **User bias**: Active voters may have different perspectives than passive readers |
| - **Source bias**: Certain news sources may be over-represented |
| - **Market condition bias**: Dataset may reflect specific market cycles (bull/bear markets) |
| - **Geographic bias**: English-speaking regions and news sources are over-represented |
| |
| ### Other Known Limitations |
| |
| - **Temporal lag**: Not suitable for real-time sentiment analysis |
| - **Market volatility**: Sentiment may change rapidly after news publication |
| - **Vote manipulation**: Despite filters, coordinated voting cannot be completely ruled out |
| - **Context dependency**: Headlines lack full article context, which may affect sentiment interpretation |
| - **Evolving terminology**: Cryptocurrency terminology and memes evolve rapidly |
| - **Static snapshot**: Current version does not capture ongoing sentiment changes |
| |
| ## Additional Information |
| |
| ### Dataset Curators |
| |
| Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca, Jiahua Xu |
| |
| ### Licensing Information |
| |
| **CC-BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International) |
| |
| This dataset is released under CC-BY-NC 4.0 for **research purposes only**. |
| |
| **Key terms:** |
| - **Attribution required**: You must give appropriate credit to the dataset creators |
| - **Non-commercial use**: Commercial use is not permitted under this license |
| - **Academic research**: The dataset is intended for academic and non-profit research |
| |
| **Legal basis:** |
| - Derived from publicly available CryptoPanic data with crowdsourced community annotations |
| - Data collected via CryptoPanic's free API between March and May 2025 |
| - To the best of our knowledge, the Terms of Service at the time of collection (cryptopanic.com/terms/) contained no restrictions on academic research use or redistribution |
| |
| For more information on CC-BY-NC 4.0, see: https://creativecommons.org/licenses/by-nc/4.0/ |
| |
| |
| ### Acknowledgments |
| |
| We thank the CryptoPanic platform and its community of users for making this dataset possible through their engagement and contributions to cryptocurrency news curation. |
| |
| ### Citation Information |
| |
| ```bibtex |
| @misc{hernandez2026dlt-corpus, |
| title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain}, |
| author={Walter Hernandez Cruz and Peter Devine and Nikhil Vadgama and Paolo Tasca and Jiahua Xu}, |
| year={2026}, |
| eprint={2602.22045}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2602.22045}, |
| } |
| ``` |
